Requirement analysis for building practical accident warning systems based on vehicular ad-hoc networks

An Accident Warning System (AWS) is a safety application that provides collision avoidance notifications for next generation vehicles whilst Vehicular Ad-hoc Networks (VANETs) provide the communication functionality to exchange these notifi- cations. Despite much previous research, there is little agreement on the requirements for accident warning systems. In order to build a practical warning system, it is important to ascertain the system requirements, information to be exchanged, and protocols needed for communication between vehicles. This paper presents a practical model of an accident warning system by stipulating the requirements in a realistic manner and thoroughly reviewing previous proposals with a view to identify gaps in this area

VANET-Based Traffic Monitoring and Incident Detection System: A Review

As a component of intelligent transport systems (ITS), vehicular ad hoc network (VANET), which is a subform of manet, has been identified. It is established on the roads based on available vehicles and supporting road infrastructure, such as base stations. An accident can be defined as any activity in the environment that may be harmful to human life or dangerous to human life. In terms of early detection, and broadcast delay. VANET has shown various problems. The available technologies for incident detection and the corresponding algorithms for processing. The present problem and challenges of incident detection in VANET technology are discussed in this paper. The paper also reviews the recently proposed methods for early incident techniques and studies them

MobileTrust: Secure Knowledge Integration in VANETs

Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy

INRISCO: INcident monitoRing in Smart COmmunities

Major advances in information and communication technologies (ICTs) make citizens to be considered as sensors in motion. Carrying their mobile devices, moving in their connected vehicles or actively participating in social networks, citizens provide a wealth of information that, after properly processing, can support numerous applications for the benefit of the community. In the context of smart communities, the INRISCO [1] proposal intends for (i) the early detection of abnormal situations in cities (i.e., incidents), (ii) the analysis of whether, according to their impact, those incidents are really adverse for the community; and (iii) the automatic actuation by dissemination of appropriate information to citizens and authorities. Thus, INRISCO will identify and report on incidents in traffic (jam, accident) or public infrastructure (e.g., works, street cut), the occurrence of specific events that affect other citizens' life (e.g., demonstrations, concerts), or environmental problems (e.g., pollution, bad weather). It is of particular interest to this proposal the identification of incidents with a social and economic impact, which affects the quality of life of citizens.This work was supported in part by the Spanish Government through the projects INRISCO under Grant TEC2014-54335-C4-1-R, Grant TEC2014-54335-C4-2-R, Grant TEC2014-54335-C4-3-R, and Grant TEC2014-54335-C4-4-R, in part by the MAGOS under Grant TEC2017-84197-C4-1-R, Grant TEC2017-84197-C4-2-R, and Grant TEC2017-84197-C4-3-R, in part by the European Regional Development Fund (ERDF), and in part by the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC)

Enhancing service quality and reliability in intelligent traffic system

Intelligent Traffic Systems (ITS) can manage on-road traffic efficiently based on real-time traffic conditions, reduce delay at the intersections, and maintain the safety of the road users. However, emergency vehicles still struggle to meet their targeted response time, and an ITS is vulnerable to various types of attacks, including cyberattacks. To address these issues, in this dissertation, we introduce three techniques that enhance the service quality and reliability of an ITS. First, an innovative Emergency Vehicle Priority System (EVPS) is presented to assist an Emergency Vehicle (EV) in attending the incident place faster. Our proposed EVPS determines the proper priority codes of EV based on the type of incidents. After priority code generation, EVPS selects the number of traffic signals needed to be turned green considering the impact on other vehicles gathered in the relevant adjacent cells. Second, for improving reliability, an Intrusion Detection System for traffic signals is proposed for the first time, which leverages traffic and signal characteristics such as the flow rate, vehicle speed, and signal phase time. Shannon’s entropy is used to calculate the uncertainty associated with the likelihood of particular evidence and Dempster-Shafer (DS) decision theory is used to fuse the evidential information. Finally, to improve the reliability of a future ITS, we introduce a model that assesses the trust level of four major On-Board Units (OBU) of a self-driving car along with Global Positioning System (GPS) data and safety messages. Both subjective logic (DS theory) and CertainLogic are used to develop the theoretical underpinning for estimating the trust value of a self-driving car by fusing the trust value of four OBU components, GPS data and safety messages. For evaluation and validation purposes, a popular and widely used traffic simulation package, namely Simulation of Urban Mobility (SUMO), is used to develop the simulation platform using a real map of Melbourne CBD. The relevant historical real data taken from the VicRoads website were used to inject the traffic flow and density in the simulation model. We evaluated the performance of our proposed techniques considering different traffic and signal characteristics such as occupancy rate, flow rate, phase time, and vehicle speed under many realistic scenarios. The simulation result shows the potential efficacy of our proposed techniques for all selected scenarios.Doctor of Philosoph

A Framework for Dynamic Traffic Monitoring Using Vehicular Ad-Hoc Networks

Traffic management centers (TMCs) need high-quality data regarding the status of roadways for monitoring and delivering up-to-date traffic conditions to the traveling public. Currently this data is measured at static points on the roadway using technologies that have significant maintenance requirements. To obtain an accurate picture of traffic on any road section at any time requires a real-time probe of vehicles traveling in that section. We envision a near-term future where network communication devices are commonly included in new vehicles. These devices will allow vehicles to form vehicular networks allowing communication among themselves, other vehicles, and roadside units (RSUs) to improve driver safety, provide enhanced monitoring to TMCs, and deliver real-time traffic conditions to drivers. In this dissertation, we contribute and develop a framework for dynamic trafficmonitoring (DTMon) using vehicular networks. We introduce RSUs called task organizers (TOs) that can communicate with equipped vehicles and with a TMC. These TOs can be programmed by the TMC to task vehicles with performing traffic measurements over various sections of the roadway. Measurement points for TOs, or virtual strips, can be changed dynamically, placed anywhere within several kilometers of the TO, and used to measure wide areas of the roadway network. This is a vast improvement over current technology. We analyze the ability of a TO, or multiple TOs, to monitor high-quality traffic datain various traffic conditions (e.g., free flow traffic, transient flow traffic, traffic with congestion, etc.). We show that DTMon can accurately monitor speed and travel times in both free-flow and traffic with transient congestion. For some types of data, the percentage of equipped vehicles, or the market penetration rate, affects the quality of data gathered. Thus, we investigate methods for mitigating the effects of low penetration rate as well as low traffic density on data quality using DTMon. This includes studying the deployment of multiple TOs in a region and the use of oncoming traffic to help bridge gaps in connectivity. We show that DTMon can have a large impact on traffic monitoring. Traffic engineers can take advantage of the programmability of TOs, giving them the ability to measure traffic at any point within several km of a TO. Most real-time traffic maps measure traffic at midpoint of roads between interchanges and the use of this framework would allow for virtual strips to be placed at various locations in between interchanges, providing fine-grained measurements to TMCs. In addition, the measurement points can be adjusted as traffic conditions change. An important application of this is end-of-queue management. Traffic engineers are very interested in deliver timely information to drivers approaching congestion endpoints to improve safety. We show the ability of DTMon in detecting the end of the queue during congestion

A Framework for Incident Detection and notification in Vehicular Ad-Hoc Networks

The US Department of Transportation (US-DOT) estimates that over half of all congestion events are caused by highway incidents rather than by rush-hour traffic in big cities. The US-DOT also notes that in a single year, congested highways due to traffic incidents cost over $75 billion in lost worker productivity and over 8.4 billion gallons of fuel. Further, the National Highway Traffic Safety Administration (NHTSA) indicates that congested roads are one of the leading causes of traffic accidents, and in 2005 an average of 119 persons died each day in motor vehicle accidents. Recently, Vehicular Ad-hoc Networks (VANET) employing a combination of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communication have been proposed to alert drivers to traffic events including accidents, lane closures, slowdowns, and other traffic-safety issues. In this thesis, we propose a novel framework for incident detection and notification dissemination in VANETs. This framework consists of three main components: a system architecture, a traffic incident detection engine and a notification dissemination mechanism. The basic idea of our framework is to collect and aggregate traffic-related data from passing cars and to use the aggregated information to detect traffic anomalies. Finally, the suitably filtered aggregated information is disseminated to alert drivers about traffic delays and incidents. The first contribution of this thesis is an architecture for the notification of traffic incidents, NOTICE for short. In NOTICE, sensor belts are embedded in the road at regular intervals, every mile or so. Each belt consists of a collection of pressure sensors, a simple aggregation and fusion engine, and a few small transceivers. The pressure sensors in each belt allow every message to be associated with a physical vehicle passing over that belt. Thus, no one vehicle can pretend to be multiple vehicles and then, is no need for an ID to be assigned to vehicles. Vehicles in NOTICE are fitted with a tamper-resistant Event Data Recorder (EDR), very much like the well-known black-boxes onboard commercial aircraft. EDRs are responsible for storing vehicles behavior between belts such as acceleration, deceleration and lane changes. Importantly, drivers can provide input to the EDR, using a simple menu, either through a dashboard console or through verbal input. The second contribution of this thesis is to develop incident detection techniques that use the information provided by cars in detecting possible incidents and traffic anomalies using intelligent inference techniques. For this purpose, we developed deterministic and probabilistic techniques to detect both blocking incidents, accidents for examples, as well as non-blocking ones such as potholes. To the best of our knowledge, our probabilistic technique is the first VANET based automatic incident detection technique that is capable of detecting both blocking and non blocking incidents. Our third contribution is to provide an analysis for vehicular traffic proving that VANETs tend to be disconnected in many highway scenarios, consisting of a collection of disjoint clusters. We also provide an analytical way to compute the expected cluster size and we show that clusters are quite stable over time. To the best of our knowledge, we are the first in the VANET community to prove analytically that disconnection is the norm rather than the exceptions in VANETs. Our fourth contribution is to develop data dissemination techniques specifically adapted to VANETs. With VANETs disconnection in mind, we developed data dissemination approaches that efficiently propagate messages between cars and belts on the road. We proposed two data dissemination techniques, one for divided roads and another one for undivided roads. We also proposed a probabilistic technique used by belts to determine how far should an incident notification be sent to alert approaching drivers. Our fifth contribution is to propose a security technique to avoid possible attacks from malicious drivers as well as preserving driver\u27s privacy in data dissemination and notification delivery in NOTICE. We also proposed a belt clustering scheme to reduce the probability of having a black-hole in the message dissemination while reducing also the operational burden if a belt is compromised